A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

Report generated on 2022-10-06, 16:51 based on data in:
/sbgenomics/workspaces/4ef75125-d0f1-40e6-8361-499ba07304b1/tasks/8ff206a4-28d3-4ab7-b795-f1fa43dd8579/multiqc_1_8

General Statistics

Sample Name% rRNA% mRNA% AlignedInsert Size% Dups% Dups% GCM Seqs
LCM1
31.7%
11.9%
53%
204 bp
83.3%
LCM10
25.4%
9.4%
43%
185 bp
92.6%
LCM101
32.4%
11.7%
47%
207 bp
82.1%
LCM101_S48_L004_R1_001
81.4%
57%
42.0
LCM101_S48_L004_R2_001
2.5%
59%
42.0
LCM10_S7_L004_R1_001
89.1%
58%
64.9
LCM10_S7_L004_R2_001
6.1%
60%
64.9
LCM11
26.1%
9.6%
48%
194 bp
85.0%
LCM11_S8_L004_R1_001
82.5%
57%
40.7
LCM11_S8_L004_R2_001
2.1%
59%
40.7
LCM15
30.5%
11.7%
50%
202 bp
80.0%
LCM15_S38_L004_R1_001
79.8%
57%
40.1
LCM15_S38_L004_R2_001
2.3%
58%
40.1
LCM17
38.1%
10.4%
52%
205 bp
82.9%
LCM17_S32_L004_R1_001
81.5%
57%
41.1
LCM17_S32_L004_R2_001
2.2%
58%
41.1
LCM18
26.3%
12.1%
49%
195 bp
81.2%
LCM18_S9_L004_R1_001
80.9%
57%
53.0
LCM18_S9_L004_R2_001
1.9%
59%
53.0
LCM19
31.0%
10.6%
50%
196 bp
84.5%
LCM19_S44_L004_R1_001
82.8%
57%
61.6
LCM19_S44_L004_R2_001
2.5%
59%
61.6
LCM1_S25_L004_R1_001
84.8%
57%
62.2
LCM1_S25_L004_R2_001
2.5%
58%
62.2
LCM2
25.8%
9.6%
43%
198 bp
88.3%
LCM2_S13_L004_R1_001
85.1%
57%
57.1
LCM2_S13_L004_R2_001
4.0%
59%
57.1
LCM30
28.4%
8.0%
44%
192 bp
87.1%
LCM30_S30_L004_R1_001
81.6%
57%
44.4
LCM30_S30_L004_R2_001
2.2%
59%
44.4
LCM4
31.1%
10.7%
46%
200 bp
83.6%
LCM40
33.4%
11.5%
52%
193 bp
83.6%
LCM40_S26_L004_R1_001
83.1%
57%
44.6
LCM40_S26_L004_R2_001
2.2%
59%
44.6
LCM42
26.6%
11.3%
48%
201 bp
80.5%
LCM42_S14_L004_R1_001
78.6%
56%
39.6
LCM42_S14_L004_R2_001
2.5%
58%
39.6
LCM43
23.5%
6.8%
46%
171 bp
90.3%
LCM43_S36_L004_R1_001
84.0%
58%
43.4
LCM43_S36_L004_R2_001
2.4%
61%
43.4
LCM44
30.5%
15.3%
54%
221 bp
74.8%
LCM44_S10_L004_R1_001
78.8%
56%
45.3
LCM44_S10_L004_R2_001
1.7%
57%
45.3
LCM46
28.5%
11.6%
48%
221 bp
80.0%
LCM46_S45_L004_R1_001
80.9%
56%
75.9
LCM46_S45_L004_R2_001
2.8%
58%
75.9
LCM4_S31_L004_R1_001
79.2%
57%
44.0
LCM4_S31_L004_R2_001
3.2%
58%
44.0
LCM51
28.3%
11.0%
47%
196 bp
83.1%
LCM51_S39_L004_R1_001
81.9%
57%
54.3
LCM51_S39_L004_R2_001
2.6%
58%
54.3
LCM54
34.5%
11.7%
57%
187 bp
81.0%
LCM54_S2_L004_R1_001
79.4%
56%
37.0
LCM54_S2_L004_R2_001
1.8%
57%
37.0
LCM55
34.6%
9.3%
53%
194 bp
85.4%
LCM55_S11_L004_R1_001
81.7%
57%
44.8
LCM55_S11_L004_R2_001
1.5%
58%
44.8
LCM57
30.5%
11.0%
46%
202 bp
86.9%
LCM57_S40_L004_R1_001
84.5%
57%
48.9
LCM57_S40_L004_R2_001
4.5%
60%
48.9
LCM58
30.1%
13.1%
44%
180 bp
93.4%
LCM58_S1_L004_R1_001
90.4%
57%
64.8
LCM58_S1_L004_R2_001
9.6%
59%
64.8
LCM6
38.8%
10.0%
53%
213 bp
84.1%
LCM61
28.9%
10.2%
50%
180 bp
84.2%
LCM61_S20_L004_R1_001
78.6%
56%
47.2
LCM61_S20_L004_R2_001
2.4%
58%
47.2
LCM64
29.4%
10.6%
42%
232 bp
84.4%
LCM64_S15_L004_R1_001
84.7%
57%
58.1
LCM64_S15_L004_R2_001
3.2%
59%
58.1
LCM65
32.5%
13.1%
51%
185 bp
83.1%
LCM65_S21_L004_R1_001
81.0%
55%
58.9
LCM65_S21_L004_R2_001
2.8%
57%
58.9
LCM66
30.9%
12.7%
45%
221 bp
86.9%
LCM66_S46_L004_R1_001
86.8%
57%
46.9
LCM66_S46_L004_R2_001
4.0%
59%
46.9
LCM67
27.5%
13.0%
50%
189 bp
79.1%
LCM67_S3_L004_R1_001
79.7%
57%
37.3
LCM67_S3_L004_R2_001
2.4%
59%
37.3
LCM69
32.0%
8.9%
51%
198 bp
86.8%
LCM69_S41_L004_R1_001
83.9%
57%
49.6
LCM69_S41_L004_R2_001
1.9%
58%
49.6
LCM6_S43_L004_R1_001
82.9%
56%
44.2
LCM6_S43_L004_R2_001
2.2%
58%
44.2
LCM7
40.0%
8.3%
54%
181 bp
87.7%
LCM70
26.7%
11.0%
49%
181 bp
84.4%
LCM70_S33_L004_R1_001
83.5%
58%
54.9
LCM70_S33_L004_R2_001
3.0%
60%
54.9
LCM71
30.8%
11.0%
55%
185 bp
82.3%
LCM71_S34_L004_R1_001
80.4%
56%
50.7
LCM71_S34_L004_R2_001
1.6%
58%
50.7
LCM72
29.1%
9.6%
48%
187 bp
85.1%
LCM72_S12_L004_R1_001
81.0%
57%
36.7
LCM72_S12_L004_R2_001
2.6%
59%
36.7
LCM74
35.7%
11.0%
53%
205 bp
80.9%
LCM74_S47_L004_R1_001
79.2%
56%
42.3
LCM74_S47_L004_R2_001
1.9%
57%
42.3
LCM75
29.6%
10.4%
48%
202 bp
85.2%
LCM75_S4_L004_R1_001
82.7%
57%
55.6
LCM75_S4_L004_R2_001
2.3%
60%
55.6
LCM76
35.3%
10.0%
48%
198 bp
87.2%
LCM76_S27_L004_R1_001
85.4%
57%
68.2
LCM76_S27_L004_R2_001
4.0%
59%
68.2
LCM78
27.9%
12.5%
53%
191 bp
80.5%
LCM78_S35_L004_R1_001
81.1%
56%
53.6
LCM78_S35_L004_R2_001
2.1%
57%
53.6
LCM7_S37_L004_R1_001
81.2%
56%
65.2
LCM7_S37_L004_R2_001
2.2%
58%
65.2
LCM8
39.8%
8.4%
51%
212 bp
86.8%
LCM82
31.4%
10.3%
44%
213 bp
85.5%
LCM82_S28_L004_R1_001
81.4%
57%
73.1
LCM82_S28_L004_R2_001
3.3%
58%
73.1
LCM83
32.5%
12.3%
55%
198 bp
80.4%
LCM83_S5_L004_R1_001
79.4%
56%
57.7
LCM83_S5_L004_R2_001
1.6%
57%
57.7
LCM84
30.6%
9.6%
51%
194 bp
83.8%
LCM84_S6_L004_R1_001
79.9%
56%
37.2
LCM84_S6_L004_R2_001
1.7%
58%
37.2
LCM85
32.3%
10.4%
48%
187 bp
91.1%
LCM85_S49_L004_R1_001
89.2%
57%
54.1
LCM85_S49_L004_R2_001
4.4%
59%
54.1
LCM86
34.5%
9.0%
52%
205 bp
86.5%
LCM86_S42_L004_R1_001
84.6%
56%
55.9
LCM86_S42_L004_R2_001
1.9%
58%
55.9
LCM87
27.6%
11.2%
44%
208 bp
85.7%
LCM87_S16_L004_R1_001
83.6%
57%
37.5
LCM87_S16_L004_R2_001
3.2%
59%
37.5
LCM88
37.3%
9.8%
51%
205 bp
83.2%
LCM88_S53_L004_R1_001
83.0%
56%
53.8
LCM88_S53_L004_R2_001
2.5%
57%
53.8
LCM8_S29_L004_R1_001
84.3%
56%
60.0
LCM8_S29_L004_R2_001
2.1%
58%
60.0
LCM9
30.1%
10.9%
51%
189 bp
84.6%
LCM90
30.3%
10.3%
53%
183 bp
83.4%
LCM90_S50_L004_R1_001
81.5%
56%
51.0
LCM90_S50_L004_R2_001
2.1%
58%
51.0
LCM91
29.1%
10.3%
47%
200 bp
84.8%
LCM91_S51_L004_R1_001
82.9%
57%
49.4
LCM91_S51_L004_R2_001
2.1%
59%
49.4
LCM92
30.0%
12.7%
49%
203 bp
82.2%
LCM92_S54_L004_R1_001
82.8%
57%
44.1
LCM92_S54_L004_R2_001
2.5%
58%
44.1
LCM94
29.3%
13.7%
56%
198 bp
76.3%
LCM94_S17_L004_R1_001
77.4%
55%
64.0
LCM94_S17_L004_R2_001
1.9%
56%
64.0
LCM95
27.8%
10.8%
52%
184 bp
83.2%
LCM95_S18_L004_R1_001
82.3%
56%
52.1
LCM95_S18_L004_R2_001
2.1%
58%
52.1
LCM96
29.4%
11.2%
44%
224 bp
83.3%
LCM96_S22_L004_R1_001
83.0%
57%
45.2
LCM96_S22_L004_R2_001
3.9%
59%
45.2
LCM97
33.0%
11.5%
51%
218 bp
79.6%
LCM97_S23_L004_R1_001
78.2%
56%
43.0
LCM97_S23_L004_R2_001
1.9%
58%
43.0
LCM98
28.9%
12.5%
50%
205 bp
79.4%
LCM98_S24_L004_R1_001
78.0%
56%
43.6
LCM98_S24_L004_R2_001
1.8%
57%
43.6
LCM99
27.1%
11.5%
44%
225 bp
80.4%
LCM99_S52_L004_R1_001
81.5%
57%
44.8
LCM99_S52_L004_R2_001
2.7%
58%
44.8
LCM9_S19_L004_R1_001
83.1%
57%
56.3
LCM9_S19_L004_R2_001
2.2%
59%
56.3

Picard

Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

Alignment Summary

Plase note that Picard's read counts are divided by two for paired-end data.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Insert Size

Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Mark Duplicates

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


RnaSeqMetrics Assignment

Number of bases in primary alignments that align to regions in the reference genome.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


RnaSeqMetrics Strand Mapping

Number of aligned reads that map to the correct strand.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Gene Coverage

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


STAR

STAR is an ultrafast universal RNA-seq aligner.

Gene Counts

Statistics from results generated using --quantMode GeneCounts. The three tabs show counts for unstranded RNA-seq, counts for the 1st read strand aligned with RNA and counts for the 2nd read strand aligned with RNA.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


FastQC

FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.
Rollover for sample name
Position: -
%T: -
%C: -
%A: -
%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Sequence Length Distribution

All samples have sequences of a single length (101bp).

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Overrepresented sequences

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).


Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

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